# Dedicated Features for Music Genre Classification

## Context

In the context of Music Genre Classification, we propose to use, as entries of a CNN, a set of eight music features chosen along three main music dimensions: dynamics, timbre and tonality.

With CNNs (Figure 1) trained in such a way that filter dimensions are interpretable in time and frequency, results show that only eight music features are as efficient than 513 frequency bins of a spectrogram and that late score fusion between systems based on both feature types improves accuracy.

## Features

**Baseline System **– **Spectrogram : **FFT on 46.44 ms analysis Hamming window. Output from each frame is a 513-dimensional vector.

**Music Features System** :

**Dynamics Feature**- Short-term energy: Metal and Classic are highly related with energy.

**Timbre Features**- Zero-crossing rate (rate of sign changes of a signal): for percussive
- Brightness (amount of energy above a cut off frequency of 15 kHz):
- Spectral Flatness (statistical moment of the power spectrum): for
- Spectral Shannon Entropy (statistical moment of the amount of
- Spectral Roughness (statistical moment of the average of all the

- Zero-crossing rate (rate of sign changes of a signal): for percussive
**Tonality Features**- Zero-crossing
- Harmonic Change Detection function (flux of the tonal centroid, which is

- Zero-crossing

**Late Fusion** :

**Single System:**each entry of a network corresponding to a 3 seconds clip, the**Fusion of the two Systems:**the probabilities of the two networks for each clip and each genre are averaged. Then the decision follows the same scheme as in the case of a single system.

## Networks

## Application

- Application was made on GTZAN Dataset
- 10 genres: 10 x 100 x 30s recordings
- Radio, compact disks, and MP3
- Format : 22.050 kHz, 16-bit, mono

- Implementation
- Python with Theano
- GPU on NVIDIA Tesla K40

- Conclusion
- 8 musical features chosen along dynamics, timbre and tonality dimensions
- CNNs: filters dimensions interpretable in time and frequency
- SPECTRO (88%) < 8 MUSIC features (90%) < FUSION (91%)

## Contributors

- Christine Sénac (contact)
- Julien Pinquier
- Florian Mouret
- Thomas Pellegrini

## Main publications

Christine Senac, Thomas Pellegrini, Florian Mouret, Julien Pinquier *Music Feature Maps with Convolutional Neural Networks for Music Genre Classification. (short paper) *Dans : *International Workshop on Content-Based Multimedia Indexing (CBMI 2017)*, *Florence, Italie*, *19/06/17-21/06/17*, ACM : Association for Computing Machinery, p. 1-5, juin 2017. Accès : https://doi.org/10.1145/3095713.3095733

Christine Senac, Thomas Pellegrini, Julien Pinquier, Florian Mouret *Réseaux de neurones convolutifs et paramètres musicaux pour la classification en genres (regular paper) *Dans : *Colloque GRETSI sur le Traitement du Signal et des Images (GRETSI 2017)*, *Juan-les-pins*, *05/09/17-08/09/17*, GRETSI CNRS, p. 1-5, septembre 2017